• Fire up your notebooks!
• Go to this page: http://bit.ly/datahandling-2021
• Use one row to respond to the questions in the column headers (see the first two rows for examples).

Data Science workflow. Source: Wickham and Grolemund (2017), licensed under the Creative Commons Attribution-Share Alike 3.0 United States license.

What could be the output of all this?

• Research report/paper (e.g., BA Thesis)
• Presentation/Slides
• Website
• Web application (interactive; alas the introductory example)
• Dashboard for management
• Recommender system (i.e., a trained machine learning algorithm)
• …

“This coupling of scientific discovery and practice involves the collection, management, processing, analysis, visualization, and interpretation of vast amounts of heterogeneous data associated with a diverse array of scientific, translational, and inter-disciplinary applications.”

University of Michigan ‘Data Science Initiative,’ 2015

“Seemingly, statistics is being marginalized here; the implicit message is that statistics is a part of what goes on in data science but not a very big part. At the same time, many of the concrete descriptions of what the DSI will actually do will seem to statisticians to be bread-and-butter statistics. Statistics is apparently the word that dare not speak its name in connection with such an initiative!”

David Donoho (2015). 50 years of Data Science

“All in all, I have come to feel that my central interest is in data analysis, which I take to include, among other things: …”

“All in all, I have come to feel that my central interest is in data analysis, which I take to include, among other things: procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data.”

• Lectures (Thursday morning)
  • Background/Concepts
  • Live demonstrations of concepts
  • Illustration of ‘hands-on’ approaches

• Lectures (Thursday morning)
  • Background/Concepts
  • Live demonstrations of concepts
  • Illustration of ‘hands-on’ approaches
• Exercises (handed out every other week)
  • Some conceptual questions (as they appear in the exam)
  • Hands-on exercises/tutorials in R
  • Detailed solution videos
  • First Exercises (set up R/RStudio) is available on StudyNet/Canvas today

• Workshops/Exercises (bi-weekly evening sessions)
  • Discussion of exercises and additional input
  • Recap of theoretical concepts
  • Q&A, support
• Guest lecture and research insights

• Lectures (every Thursday morning)
  • Background/Concepts
  • Live demonstrations of concepts
  • Illustration of ‘hands-on’ approaches
• Workshops/Exercises (bi-weekly evening sessions)
  • Guided tutorials
  • Discussion of homework exercises
  • Recap of theoretical concepts
  • First Exercises (set up R/RStudio) is available on StudyNet/Canvas today

• Learning mode in this course: Visit the lecture, recap key concepts in lecture notes (self-study), work on exercises, watch solution video, come to exercise session, repeat…

• Strongly encouraged: (virtual) learning groups!

  • Biweekly exercises provide opportunity.
  • Tackle the tricky exercises together!

• All information and materials (notes, slides, course sheet, syllabus, etc.) are available on StudyNet/Canvas.
• Exercises will be handed out via GitHub Classroom!
• Solutions to the exercises will be made available on Canvas.
• This course is open souce: all raw materials (code, source code for slides, notes, etc.) are freely available on GitHub

Murrell, Paul (2009). Introduction to Data Technologies, London: Chapman & Hall/CRC.

Wickham, Hadley and Garred Grolemund (2017). R for Data Science, 1st Edition. Sebastopol, CA: O’Reilly.

• Stackoverflow
• Get inspired in the R blogsphere

• Central, written examination.
• Multiple choice questions.
• A few open questions.
• Theoretical concepts and practical applications in R (questions based on code examples).

• Exercises towards the end of the term will contain sample questions.
  • Get familiar with the style/format of questions.
• Exchange students who need to take the exam before the central exam block:
  • Questions: michael.tueting@unisg.ch
  • Decentral exam for exchange students: TBD.